COVID-19 Vaccine Rollout

Our World In Data Vaccine Plots

rm(list=ls())
source("../DATA/movavg.R")
db <- db <- dbConnect(RSQLite::SQLite(),dbname= "../COVID-19-DB/OURWORLD.sqlite3")
SWE <- dbGetQuery(db,"select * from OWID")
SWE$date <- as.Date(SWE$date)
SWE <- SWE[order(SWE$date),]
df <- SWE %>%select(date,location,iso_code,new_vaccinations,
                    new_vaccinations_smoothed,total_vaccinations,population)
                    
# df$VMA <- ma(df$new_vaccinations,6,centre=TRUE)

dbDisconnect(db)

World <- df %>% filter(location=="World" & date >="2020-12-10") 

Plot of New Vaccinations for the World by Day

ggplot(World) + geom_line(aes(x=date,y=total_vaccinations)) +
  scale_y_continuous(labels=comma) + 
  labs(title="Worldwide Total Vaccinations")

## Warning: Removed 5 row(s) containing missing values (geom_path).

Total New Vaccinations by Country

vaccine_date <- df %>% filter(location !="World") %>% na.omit() %>%
  group_by(date) %>% summarise(Total = sum(new_vaccinations))

  ggplot(vaccine_date) + geom_line(aes(x=date,y=Total)) + geom_smooth(aes(x=date,y=Total)) +
    labs(title="Worldwide New Vaccinations by Date") +
    scale_y_continuous(labels=comma)

Total Vaccinations by County (Top 5)

vaccine_country <- df %>% filter(location !="World") %>% na.omit() %>%
  group_by(location) %>% summarise(Total = sum(new_vaccinations)) %>%
  arrange %>% top_n(5,Total)

 ggplot(vaccine_country) + geom_col(aes(x=location,y=Total)) +
  scale_y_continuous(labels=comma) +
  labs(title="Top Five Countries by New Vaccinations")

Plot of New and Total Vaccination for Israel

Israel <- df %>% filter(location =="Israel") %>% na.omit()

p1 <- ggplot(Israel) + geom_line(aes(x=date,y=new_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="Israel New Vaccinations By Date") +
  geom_smooth(aes(x=date,y=new_vaccinations))

p2 <- ggplot(Israel) + geom_line(aes(x=date,y=total_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="Israel Total Vaccinations By Date") 

Israel$Rate <- Israel$total_vaccinations/Israel$population
p3 <- ggplot(Israel) + geom_line(aes(x=date,y=Rate)) +
  scale_y_continuous(labels=percent) +
  labs(title="Israel vaccination Rate By Day")


ggplotly(p1)

ggplotly(p2)

ggplotly(p3)

US Vaccnations By Date

US <- df %>% filter(location =="United States") %>% na.omit()
summary(US$new_vaccinations)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   57909  651669  878059  886713 1125552 1561585

p5 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="US New Vaccinations By Date") +
  geom_smooth(aes(x=date,y=new_vaccinations))

p6 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="US Total Vaccinations By Date") 

US$Rate <- US$total_vaccinations/US$population
p7 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
  scale_y_continuous(labels=percent) +
  labs(title="US vaccination Rate By Day")
ggplotly(p5)

ggplotly(p6)

ggplotly(p7)

Spain Vaccnations By Date

US <- df %>% filter(location =="Spain") %>% na.omit()

p8 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="Spai nNew Vaccinations By Date") +
  geom_smooth(aes(x=date,y=new_vaccinations))

p9 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="Spain Total Vaccinations By Date") 
ggplotly(p8)

ggplotly(p9)

US$Rate <- US$total_vaccinations/US$population
p10 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
  scale_y_continuous(labels=percent) +
  labs(title="Spain vaccination Rate By Day")
ggplotly(p10)

United Kingdom Vaccnations By Date

US <- df %>% filter(location =="United Kingdom") %>% na.omit()

p8 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="United Kingdom New Vaccinations By Date") +
  geom_smooth(aes(x=date,y=new_vaccinations))

p9 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="United Kingdom Total Vaccinations By Date") 
ggplotly(p8)

ggplotly(p9)

US$Rate <- US$total_vaccinations/US$population
p10 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
  scale_y_continuous(labels=percent) +
  labs(title="Kingdom vaccination Rate By Day")
ggplotly(p10)

India Vaccnations By Date

US <- df %>% filter(location =="India") %>% na.omit()

p8 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="India New Vaccinations By Date") +
  geom_smooth(aes(x=date,y=new_vaccinations))

p9 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="India Total Vaccinations By Date") 
ggplotly(p8)

ggplotly(p9)

US$Rate <- US$total_vaccinations/US$population
p10 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
  scale_y_continuous(labels=percent) +
  labs(title="India vaccination Rate By Day")
ggplotly(p10)

Germany Vaccnations By Date

US <- df %>% filter(location =="Germany") %>% na.omit()

p8 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="Germany New Vaccinations By Date") +
  geom_smooth(aes(x=date,y=new_vaccinations))

p9 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="Germany Total Vaccinations By Date") 
ggplotly(p8)

ggplotly(p9)

US$Rate <- US$total_vaccinations/US$population
p10 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
  scale_y_continuous(labels=percent) +
  labs(title="Germany vaccination Rate By Day")
ggplotly(p10)